https://github.com/clementsan/object_detection_yolo_fastapi

Object detection with YOLO model and FastAPI
https://github.com/clementsan/object_detection_yolo_fastapi

computer-vision data-science deep-learning fastapi machine-learning object-detection object-detection-api yolo

Last synced: about 2 months ago
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Object detection with YOLO model and FastAPI

• Host: GitHub
• URL: https://github.com/clementsan/object_detection_yolo_fastapi
• Owner: clementsan
• Created: 2025-01-22T22:10:00.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2025-01-22T22:47:02.000Z (4 months ago)
• Last Synced: 2025-01-22T23:21:48.592Z (4 months ago)
• Topics: computer-vision, data-science, deep-learning, fastapi, machine-learning, object-detection, object-detection-api, yolo
• Language: Python
• Homepage:
• Size: 209 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Object detection via FastAPI

[![](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)

[![Docker Pulls](https://img.shields.io/docker/pulls/cvachet/object-detection-yolo-fastapi)](https://hub.docker.com/repository/docker/cvachet/object-detection-yolo-fastapi)

[![code style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

[![linting: pylint](https://img.shields.io/badge/linting-pylint-yellowgreen)](https://github.com/pylint-dev/pylint)

![example workflow](https://github.com/clementsan/object_detection_yolo_fastapi/actions/workflows/ci_python.yml/badge.svg)

![example workflow](https://github.com/clementsan/object_detection_yolo_fastapi/actions/workflows/publish_docker_image.yml/badge.svg)

**Aim:** AI-driven object detection via FastAPI (on COCO image dataset)

**Machine learning models:**

 - ultralytics/yolo11n

 - ultralytics/yolo11s

-----

### Table of contents:

 - [Local development](#1-local-development)

   - [Run FastAPI interface](#11-run-fastapi-interface)

   - [Run API query via curl command](#12-run-api-query-via-curl-command)

   - [Run API query via python script](#13-run-api-query-via-python-script)

   - [Tests via pytest library](#14-tests-via-pytest-library)

 - [Execution via docker container](#2-execution-via-docker-container)

   - [Build image and run docker container](#21-build-image-and-run-docker-container)

   - [Run query via API](#22-run-query-via-api)

 - [Deployment on Docker hub](#3-deployment-on-docker-hub)

 - [MLOps pipeline via GitHub actions](#4-mlops-pipeline-via-github-actions)

----

## 1. Local development

### 1.1. Run FastAPI interface

Command line in development mode:

> fastapi dev app/main.py

Manual command line in production:

> fastapi run app/main.py

Notes:

 - Serving at: http://127.0.0.1:8000 

 - API docs: http://127.0.0.1:8000/docs

### 1.2. Run API query via curl command

Command lines:

 - Endpoint "/":

> curl -X 'GET' -H 'accept: application/json' 'http://127.0.0.1:8000/'

- Endpoint "/info":

> curl -X 'GET' -H 'accept: application/json' 'http://127.0.0.1:8000/info'

  

 - Endpoint "/api/v1/detect":

>  curl -X POST -F "image=@./tests/data/savanna.jpg" http://127.0.0.1:8000/api/v1/detect

 - Endpoint "/api/v1/detect" with optional model type (e.g. yolo11n or yolo11s):

>  curl -X POST -F "image=@./tests/data/savanna.jpg" http://127.0.0.1:8000/api/v1/detect?model=yolo11n

### 1.3. Run API query via python script

Command line:

> python app/inference_api.py -u "http://127.0.0.1:8000/api/v1/detect" -f tests/data/savanna.jpg

### 1.4. Tests via pytest library

Command lines:

> pytest tests/ -v

> pytest tests/ -s -o log_cli=true -o log_level=DEBUG

## 2. Execution via docker container

### 2.1. Build image and run docker container

Command lines:

> sudo docker build -t object-detection-yolo-fastapi .

> sudo docker run --name object-detection-yolo-fastapi-cont -p 8000:8000 object-detection-yolo-fastapi

### 2.2. Run query via API

Command lines:

 - Endpoint "/":

> curl -X 'GET' 'http://0.0.0.0:8000/' -H 'accept: application/json'

 - Endpoint "/info":

> curl -X 'GET' -H 'accept: application/json' 'http://0.0.0.0:8000/info'

 - Endpoint "/api/v1/detect":

>  curl -X 'POST' -F "image=@./tests/data/savanna.jpg" http://0.0.0.0:8000/api/v1/detect 

 - Endpoint "/api/v1/detect" with optional model type (e.g. yolo11n or yolo11s):

>  curl -X 'POST' -F "image=@./tests/data/savanna.jpg" http://0.0.0.0:8000/api/v1/detect\?model=yolo11s

## 3. Deployment on Docker hub

This FastAPI application is available as a Docker container on Docker hub

URL: https://hub.docker.com/r/cvachet/object-detection-yolo-fastapi

## 4. MLOps pipeline via GitHub actions

Github actions were created to enable Continuous Integration (CI) and Continuous Deployment (CD) for this FastAPI app. 

YAML files:

 - Python testing suite: [ci_python.yml](.github/workflows/ci_python.yml)

 - Pushing to docker: [publish_docker_image.yml](.github/workflows/publish_docker_image.yml)